ADMM has been used for both convex [1]} and non-convex
optimization [2]} and various ADMM enhancements have been
proposed. We demonstrate how these improve ADMM for (REF ). For
initial evaluations, we use the artificial objective
(Appendix ) for cheap evaluations, which allow us to
efficiently generate statistically significant results over multiple trials. A
maximum of 100 ADMM iterations is executed with \(\rho =1\)  (ADMM for CASH appears
to be stable with respect to \(\rho \) ; see Appendix ) for
a maximum runtime of \(2^{10}\)  seconds. We consider a search space of 4 modules
with 8 scalers, 11 transformers, 7 feature selectors and 11 estimators (total
6776 algorithm combinations with almost 100 HPs). See [3]} for complete details.
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